Abstract

This webpage will show the results of a bat survey study done in the Plumas National Forest in North California. The objective of this study is to determine the distribution of the different species of bats within the park. In order to do that we have performed occupancy models for the species present in the park. The results of this models will be shown as maps showing the probability of occurence of bats in each point, that is, if you see a value of 1, there is a 100% chance of finding a bat in that point, if there is a value of 0 there is 0% chance of finding that specie in that point, if there is a value of 0.5 there is a 50% chance of finding that specie in that point.

Another result

Results collected in the field

Maps showing the sampled Points

Results of species prescence

In this area 0 means absence, and 1 means prescence. This table has for each site (ID), every specie and day, so for example if Mylu1=0, that means that for Myotis lucifugus (common name Little Brown bat, was detected on day one for that particular site).

Here is a key for bat species

  • Myotis yumanensis (Myyu)
  • Myotis californicus (Myca)
  • Myotis ciliolabrum (Myci)
  • Myotis volans (Myvo)
  • Myotis lucifugus (Mylu)
  • Parastrellus hesperus (Pahe)
  • Lasiurus blossevillii (Labo)
  • Myotis evotis (Myev)
  • Antrozous pallidus (Anpa)
  • Eptesicus fuscus (Epfu)
  • Lasionycteris noctivagans (Lano)
  • Myotis thysanodes (Myth)
  • Tadarida brasiliensis (Tabr)
  • Lasiurus cinereus (Laci)
  • Corynorhinus townsendii (Coto)
  • Euderma maculatum (Euma)
  • Eumops perotis (Eupe)

Maps predicting the distribution of bats

Yuma myotis (Myotis yumanensis)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10
psi(Int) -1.61 -1.64 -1.59 -1.22 -1.20 -1.56 -2.00 -1.26 -1.40 -1.13
(1.05) (1.12) (1.08) (1.01) (0.95) (1.01) (1.13) (1.01) (1.00) (1.09)
psi(Burn.intensity.Canopy) 210.29 300.25 144.63
(211.00) (268.39) (197.34)
p(Int) -3.84** -5.57** -4.56*** -3.48** -2.61* -2.83* -3.75** -2.46 -2.90* -5.52**
(1.19) (2.04) (1.33) (1.13) (1.08) (1.29) (1.17) (1.29) (1.30) (2.05)
p(Meantemp) 0.14 0.16* 0.18* 0.12 0.15* 0.14 0.14 0.15 0.16*
(0.07) (0.08) (0.08) (0.07) (0.08) (0.07) (0.08) (0.08) (0.08)
p(Meanhum) 0.02 0.02 0.02
(0.02) (0.01) (0.02)
psi(Burn.intensity.soil) 178.85 114.54 314.30 181.07
(203.84) (115.88) (23730.91) (210.14)
psi(Burn.intensity.basal) 105.76 -66.08 270.53 137.59 81.22
(197.21) (67.12) (23730.91) (197.28) (196.04)
p(sdtemp) -0.39 -0.47 -0.33
(0.31) (0.33) (0.30)
Log Likelihood -31.93 -30.66 -32.27 -32.50 -31.29 -31.46 -31.49 -31.50 -31.50 -31.50
AICc 72.91 73.04 73.59 74.05 74.29 74.54 74.60 74.62 74.63 74.72
Delta 0.00 0.12 0.68 1.14 1.38 1.62 1.69 1.71 1.72 1.80
Weight 0.06 0.06 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.02
Num. obs. 41 41 41 41 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

California bat (Myotis californicus)

Total model

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5
psi(Int) 0.97** 0.65 0.98** 0.72 0.75
(0.37) (0.45) (0.37) (0.45) (0.44)
p(Int) 4.76*** 4.78*** 2.72** 4.77*** 4.77***
(1.38) (1.38) (0.83) (1.37) (1.37)
p(Meanhum) -0.04** -0.04** -0.03** -0.04** -0.04**
(0.01) (0.01) (0.01) (0.01) (0.01)
p(sdtemp) -0.43* -0.44* -0.44* -0.44*
(0.22) (0.22) (0.22) (0.22)
psi(Burn.intensity.soil) 0.30
(0.29)
psi(Burn.intensity.Canopy) 0.23
(0.28)
psi(Burn.intensity.basal) 0.15
(0.20)
Log Likelihood -70.68 -70.06 -72.74 -70.26 -70.33
AICc 150.47 151.83 152.13 152.23 152.37
Delta 0.00 1.35 1.66 1.76 1.89
Weight 0.09 0.05 0.04 0.04 0.04
Num. obs. 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

Western Small Footed Myotis (Myotis ciliolabrum)

Statistical models
Model 1 Model 2
psi(Int) -2.36* -2.31*
(1.02) (1.01)
psi(Burn.intensity.basal) -67.74 -51.42
(84.91) (133.25)
psi(Burn.intensity.Canopy) 110.81 83.80
(139.22) (219.37)
psi(Burn.intensity.soil) -19.33 -14.16
(25.52) (41.07)
p(Int) 3.96** 5.52*
(1.52) (2.17)
p(Meantemp) -0.33** -0.38**
(0.11) (0.12)
p(sdhum) -0.09
(0.09)
Log Likelihood -27.85 -27.21
AICc 70.04 71.80
Delta 0.00 1.76
Weight 0.08 0.03
Num. obs. 41 41
p < 0.001, p < 0.01, p < 0.05

Hairy-winged bat (Myotis volans)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5
psi(Int) -0.33 0.04 0.05 0.33 -0.63
(0.73) (0.97) (0.93) (1.16) (0.65)
p(Int) -0.50 3.73 3.92 5.30 -2.06
(1.11) (4.24) (3.96) (4.17) (1.72)
p(Meanhum) -0.01 -0.01
(0.01) (0.01)
p(Julian) -0.03 -0.03 -0.03
(0.02) (0.02) (0.02)
psi(Burn.intensity.basal) -45.19
(42.17)
psi(Burn.intensity.Canopy) 60.74
(56.54)
p(Meantemp) 0.09
(0.14)
Log Likelihood -39.83 -39.96 -37.51 -38.88 -40.38
AICc 86.31 86.54 86.65 86.88 87.38
Delta 0.00 0.23 0.34 0.57 1.07
Weight 0.09 0.08 0.08 0.07 0.05
Num. obs. 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

Little Brown bat (Myotis lucifugus)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
psi(Int) -1.36** -1.31** -1.34** -1.26* -1.31* -1.32** -1.36** -1.27*
(0.52) (0.51) (0.52) (0.52) (0.51) (0.50) (0.52) (0.52)
psi(Burn.intensity.Canopy) 0.56 0.61* 0.40 0.55 1.39
(0.29) (0.29) (0.24) (0.29) (1.09)
p(Int) -1.81 -1.92 3.76 -1.73 3.85 3.10** -4.47* -1.83
(2.41) (2.38) (5.57) (2.55) (5.55) (1.11) (1.79) (2.33)
p(Meanhum) -0.02 -0.02 -0.02 -0.02 -0.02 -0.04* -0.02
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
p(Meantemp) 0.27* 0.27* 0.27* 0.26* 0.28* 0.34** 0.27*
(0.13) (0.13) (0.13) (0.13) (0.13) (0.12) (0.12)
psi(Burn.intensity.basal) 0.42 0.47*
(0.23) (0.23)
p(Julian) -0.03 -0.03
(0.03) (0.03)
psi(Burn.intensity.soil) 0.47 -0.89
(0.28) (1.10)
p(sdhum) 0.02
(0.07)
Log Likelihood -39.32 -39.45 -38.65 -40.10 -38.74 -41.52 -40.26 -38.90
AICc 90.35 90.61 91.77 91.91 91.95 92.15 92.24 92.26
Delta 0.00 0.26 1.42 1.56 1.60 1.80 1.89 1.91
Weight 0.06 0.05 0.03 0.03 0.03 0.02 0.02 0.02
Num. obs. 41 41 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

Western Red Bat (Lasiurus blossevillii)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12 Model 13 Model 14 Model 15
psi(Int) 0.12 0.01 0.17 -0.04 0.02 -0.00 0.09 0.05 0.30 1.17 0.30 2.77 -0.07 -0.06 -0.32
(1.04) (1.05) (1.02) (0.95) (1.19) (1.12) (1.09) (1.05) (1.92) (3.00) (1.46) (0.99) (1.00) (0.94)
psi(Burn.intensity.basal) 24.58 33.01 32.52 25.98 30.57 20.13
(30.63) (46.84) (42.34) (36.91) (42.08) (23.76)
psi(Burn.intensity.soil) -35.11 -46.92 -15.73 -19.75 -39.44 -46.62 -35.58 -35.31 -61.73 -43.99 -28.23 -31.42 -59.32
(44.80) (66.55) (12.52) (17.77) (109.00) (60.12) (40.63) (49.91) (279.47) (59.66) (34.54) (32.79) (77.12)
p(Int) 10.87 7.59 7.91 7.53 -1.20 -1.24 6.10 7.11 -2.02 7.66 -2.00 7.45 6.20 6.05 -3.13
(5.86) (5.13) (5.44) (5.13) (0.89) (0.78) (5.75) (5.80) (1.48) (5.38) (1.34) (4.50) (5.60) (5.40) (1.85)
p(Julian) -0.07* -0.05 -0.06 -0.05 -0.06 -0.07* -0.06 -0.06* -0.05 -0.05
(0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03)
p(Meanhum) 0.01 0.01 0.02 0.02 0.00 0.01 0.00
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
p(sdhum) -0.02 -0.02 -0.05 -0.05 0.02 -0.01 -0.01 -0.04
(0.06) (0.07) (0.07) (0.06) (0.06) (0.08) (0.08) (0.07)
psi(Burn.intensity.Canopy) 13.91 17.03 33.79 31.33 52.15 27.64 53.01
(10.98) (14.89) (88.87) (36.61) (231.31) (29.32) (69.51)
p(Meantemp) 0.15 0.15 0.14 0.14 0.13
(0.12) (0.13) (0.13) (0.13) (0.11)
Log Likelihood -26.32 -26.37 -26.66 -26.73 -28.11 -28.12 -25.39 -25.42 -28.31 -29.71 -28.45 -29.79 -25.78 -25.78 -27.28
AICc 67.12 67.21 67.78 67.93 67.94 67.96 68.18 68.24 68.33 68.53 68.61 68.69 68.96 68.96 69.04
Delta 0.00 0.09 0.67 0.82 0.83 0.84 1.07 1.12 1.22 1.42 1.49 1.57 1.85 1.85 1.92
Weight 0.04 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Num. obs. 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

Long-eared Bat (Myotis evotis)

Total model

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
psi(Int) 0.63 0.65 0.64 0.69 0.60 1.26**
(0.50) (0.50) (0.54) (0.54) (0.49) (0.47)
psi(Burn.intensity.soil) 0.89 0.76
(0.81) (0.61)
p(Int) 1.49* 1.49* 2.48 1.38* -1.24 1.42*
(0.65) (0.65) (1.29) (0.63) (2.90) (0.67)
p(Meanhum) -0.02* -0.02* -0.02* -0.02* -0.02* -0.02
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
psi(Burn.intensity.Canopy) 0.77 49.35
(0.63) (100.46)
psi(Burn.intensity.basal) -31.21 5.56
(61.72)
p(Meantemp) -0.05
(0.05)
p(Julian) 0.01
(0.02)
Log Likelihood -75.99 -76.06 -73.95 -76.72 -75.44 -78.06
AICc 161.09 161.23 162.38 162.55 162.60 162.76
Delta 0.00 0.14 1.28 1.45 1.51 1.66
Weight 0.15 0.14 0.08 0.07 0.07 0.07
Num. obs. 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

Pallid Bat (Antrozous pallidus)

Statistical models
Model 1 Model 2 Model 3
psi(Int) -1.63* -1.48 -1.46
(0.82) (0.85) (0.81)
psi(Burn.intensity.Canopy) 26.71 34.71
(31.98) (56.88)
psi(Burn.intensity.soil) -31.00 -40.71 -18.74
(38.41) (68.27) (16.45)
p(Int) 18.34* 23.28 15.90*
(9.06) (12.34) (8.07)
p(Julian) -0.12* -0.13* -0.11*
(0.06) (0.07) (0.05)
p(Meanhum) 0.05 0.04 0.04
(0.03) (0.03) (0.02)
p(Meantemp) -0.17
(0.15)
psi(Burn.intensity.basal) 13.47
(11.40)
Log Likelihood -21.00 -20.13 -21.81
AICc 56.47 57.66 58.10
Delta 0.00 1.19 1.63
Weight 0.22 0.12 0.10
Num. obs. 41 41 41
p < 0.001, p < 0.01, p < 0.05

Fringed Bat (Myotis thysanoides)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9
psi(Int) -0.06 -0.20 -1.06 -0.99 -1.11 -1.00 -1.06 -0.98 -1.11
(1.19) (1.03) (1.23) (1.21) (1.38) (1.28) (1.22) (1.21) (1.41)
p(Int) -23.71 -26.24* -21.36 -21.25 -19.06 -22.68 -26.89* -26.00* -24.70*
(13.80) (11.08) (11.47) (11.31) (10.38) (11.67) (11.40) (11.16) (11.01)
p(Julian) 0.11 0.12* 0.10 0.10 0.09 0.11* 0.13* 0.12* 0.11*
(0.06) (0.05) (0.05) (0.05) (0.05) (0.06) (0.06) (0.06) (0.05)
p(Meanhum) -0.01 -0.02 -0.02 -0.02 -0.02
(0.03) (0.02) (0.02) (0.02) (0.02)
p(sdhum) -0.01 -0.02 -0.02 -0.01
(0.09) (0.08) (0.07) (0.07)
psi(Burn.intensity.Canopy) 1.11 21.87 0.97
(1.21) (33.22) (1.16)
psi(Burn.intensity.basal) 0.89 0.80
(1.03) (1.14)
psi(Burn.intensity.soil) 2.49 -11.66 2.35
(2.79) (19.12) (2.64)
Log Likelihood -20.21 -20.24 -19.24 -19.27 -19.30 -18.06 -19.49 -19.57 -19.62
AICc 49.53 49.60 50.19 50.25 50.31 50.59 50.69 50.85 50.95
Delta 0.00 0.07 0.66 0.72 0.78 1.06 1.16 1.32 1.42
Weight 0.06 0.06 0.05 0.04 0.04 0.04 0.04 0.03 0.03
Num. obs. 41 41 41 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

Townsend’s Long-eared Bat (Corynorhinus townsendii)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9
psi(Int) -1.46 0.79 -1.56 -1.53 -1.22 1.53 -1.40 -0.54 1.61
(1.34) (3.14) (1.25) (1.32) (1.42) (5.36) (1.21) (1.51) (4.44)
psi(Burn.intensity.basal) -19.01 -20.18 -22.13 -33.77
(17.38) (20.92) (26.13) (39.73)
psi(Burn.intensity.soil) 32.38 34.50 37.67 33.91 15.74
(29.02) (34.95) (43.39) (31.92) (16.41)
p(Int) -4.78** -5.36* 4.25 -8.32* -5.13** -9.30* 6.45 -5.43** 0.78
(1.77) (2.23) (6.78) (3.52) (1.88) (3.62) (7.52) (1.97) (5.86)
p(Meanhum) 0.04 0.04 0.04 0.06* 0.04 0.06* 0.06* 0.04 0.04
(0.02) (0.02) (0.02) (0.03) (0.02) (0.03) (0.03) (0.02) (0.02)
p(Julian) -0.05 -0.08* -0.03
(0.04) (0.04) (0.03)
p(Meantemp) 0.14 0.15 0.18
(0.12) (0.11) (0.12)
psi(Burn.intensity.Canopy) -27.31 46.63
(25.87) (54.79)
Log Likelihood -18.71 -21.67 -17.79 -17.85 -19.32 -20.62 -17.97 -19.40 -20.87
AICc 49.14 49.98 50.05 50.18 50.35 50.35 50.40 50.52 50.85
Delta 0.00 0.84 0.91 1.04 1.20 1.21 1.26 1.37 1.71
Weight 0.08 0.06 0.05 0.05 0.05 0.05 0.04 0.04 0.04
Num. obs. 41 41 41 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

The western pipistrelle (Parastrellus hesperus)

big brown bat (Eptesicus fuscus)

Statistical models
Model 1 Model 2 Model 3 Model 4
psi(Int) -0.65 -0.65 -0.69 -0.72
(0.70) (0.59) (0.70) (0.61)
psi(Burn.intensity.basal) -44.80 -60.62
(51.07) (73.36)
psi(Burn.intensity.Canopy) 65.47 0.53 88.81
(76.22) (0.31) (109.04)
p(Int) 6.07** 6.66** 5.39** 6.53**
(2.03) (2.34) (2.07) (2.34)
p(Meanhum) -0.04** -0.05** -0.04** -0.05**
(0.02) (0.02) (0.02) (0.02)
p(Meantemp) -0.30** -0.28* -0.28** -0.27*
(0.09) (0.11) (0.09) (0.11)
p(sdhum) 0.05
(0.05)
psi(Burn.intensity.soil) 0.54
(0.29)
Log Likelihood -44.92 -47.20 -44.41 -47.27
AICc 104.31 106.12 106.20 106.25
Delta 0.00 1.81 1.89 1.94
Weight 0.16 0.06 0.06 0.06
Num. obs. 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

silver-haired bat (Lasionycteris noctivagans)

Statistical models
Model 1 Model 2
psi(Int) -0.06 -0.17
(0.63) (0.64)
psi(Burn.intensity.basal) -83.58 -614.20
(138.59)
psi(Burn.intensity.Canopy) 117.24 847.03
(193.98)
p(Int) 1.01 1.09
(1.10) (1.10)
p(Meantemp) -0.19** -0.19**
(0.07) (0.07)
p(sdhum) -0.09 -0.08
(0.06) (0.06)
p(sdtemp) 0.73** 0.71*
(0.28) (0.27)
psi(Burn.intensity.soil) 16.34
Log Likelihood -56.62 -56.06
AICc 130.63 132.62
Delta 0.00 2.00
Weight 0.11 0.04
Num. obs. 41 41
p < 0.001, p < 0.01, p < 0.05

Brazilian free-tailed bat (Tadarida brasiliensis)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12 Model 13
psi(Int) 1.04 0.88 0.92 0.83 0.95 0.94 0.75 0.74 0.71 1.09 2.07 0.99 1.47*
(0.65) (0.54) (0.63) (0.53) (0.64) (0.60) (0.59) (0.59) (0.58) (0.70) (1.07) (0.61) (0.57)
psi(Burn.intensity.basal) 11.99 10.80 0.85 24.21 24.23 28.05 12.05
(9.79) (8.74) (0.93) (20.85) (20.93) (65.91) (9.84)
psi(Burn.intensity.Canopy) -13.75 -12.47 -32.24 1.12 -31.51 26.12 -13.81
(10.98) (9.86) (27.21) (1.24) (27.04) (58.26) (11.04)
p(Int) 1.92 3.41 2.29 3.73 2.28 1.66 0.22 0.25 0.19 2.06 2.19 4.84 0.31
(1.43) (2.86) (1.32) (2.86) (1.31) (1.40) (0.62) (0.62) (0.62) (1.24) (1.30) (3.20) (0.68)
p(Meanhum) -0.01 -0.00 -0.01 -0.01 -0.01 -0.01 -0.00 -0.00 -0.00 -0.01 -0.01 -0.01 -0.00
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
p(Meantemp) -0.07 -0.10 -0.10 -0.06 -0.10 -0.11 -0.08
(0.07) (0.06) (0.06) (0.07) (0.05) (0.06) (0.07)
p(Julian) -0.02 -0.02 -0.02
(0.01) (0.01) (0.01)
psi(Burn.intensity.soil) 4.09 3.45 25.12 2.38
(4.12) (3.83) (109.79)
Log Likelihood -75.81 -75.85 -77.29 -74.52 -77.46 -74.73 -78.95 -78.95 -78.96 -77.87 -79.25 -75.21 -80.70
AICc 166.09 166.17 166.29 166.44 166.63 166.85 167.00 167.01 167.03 167.46 167.60 167.82 168.05
Delta 0.00 0.08 0.20 0.35 0.54 0.76 0.91 0.92 0.94 1.37 1.52 1.73 1.96
Weight 0.08 0.08 0.07 0.07 0.06 0.05 0.05 0.05 0.05 0.04 0.04 0.03 0.03
Num. obs. 41 41 41 41 41 41 41 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

hoary bat (Lasiurus cinereus)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
psi(Int) 0.90 0.61 0.66 0.43 0.56 0.40
(0.57) (0.47) (0.49) (0.40) (0.45) (0.39)
p(Int) 2.98 4.50 -2.36 -0.45 5.52 0.07
(3.17) (3.26) (1.32) (0.77) (3.31) (0.49)
p(Julian) -0.03 -0.03 -0.03
(0.02) (0.02) (0.02)
p(Meanhum) 0.02 0.01 0.02 0.01
(0.01) (0.01) (0.01) (0.01)
p(sdtemp) 0.43 0.37
(0.23) (0.23)
p(sdhum) 0.04 0.01
(0.05) (0.05)
Log Likelihood -66.27 -67.98 -68.08 -69.33 -68.15 -69.64
AICc 144.25 145.08 145.27 145.32 145.41 145.94
Delta 0.00 0.83 1.02 1.07 1.17 1.69
Weight 0.07 0.05 0.04 0.04 0.04 0.03
Num. obs. 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

Spotted bat (Euderma maculatum)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
psi(Int) 8.20 8.02 -0.51 -0.42 -0.08 5.90
(85.57) (67.46) (2.26) (2.32) (1.17) (21.85)
p(Int) 1.29 -0.25 1.52 1.52 4.74 5.58
(2.16) (2.62) (2.16) (2.17) (4.57) (6.46)
p(Meanhum) -0.11 -0.13 -0.10 -0.10 -0.26 -0.11
(0.06) (0.08) (0.06) (0.06) (0.19) (0.06)
p(sdtemp) 0.62
(0.50)
psi(Burn.intensity.basal) 0.68
(0.87)
psi(Burn.intensity.Canopy) 0.87
(1.20)
p(sdhum) 0.35
(0.28)
p(Julian) -0.02
(0.03)
Log Likelihood -10.75 -9.88 -10.47 -10.49 -10.50 -10.51
AICc 28.14 28.87 30.05 30.10 30.11 30.13
Delta 0.00 0.73 1.91 1.96 1.97 1.99
Weight 0.09 0.06 0.03 0.03 0.03 0.03
Num. obs. 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

western mastiff bat (Eumops perotis)

Statistical models
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
psi(Int) -1.71 5.15 -1.80 4.21 3.14 -1.69 4.24
(1.87) (5.79) (1.59) (4.62) (3.32) (1.53) (4.88)
psi(Burn.intensity.basal) -112.34 142.33 -130.52 113.13 92.39 -124.73 107.12
(84.85) (150.37) (82.42) (124.42) (98.33) (79.84) (124.97)
psi(Burn.intensity.Canopy) 112.69 129.98 124.13
(83.54) (81.35) (78.65)
p(Int) 0.48 -1.28 -0.32 -0.91 -2.57 0.12 -1.58
(1.47) (0.75) (1.70) (1.16) (1.41) (1.60) (1.03)
p(Meantemp) -0.14 -0.21 0.08 -0.16
(0.10) (0.12) (0.11) (0.11)
psi(Burn.intensity.soil) -103.56 -82.39 -67.20 -78.10
(109.41) (90.64) (71.61) (91.19)
p(sdhum) -0.03 0.07
(0.06) (0.09)
p(sdtemp) 0.53 -0.03
(0.42) (0.26)
p(Meanhum) -0.01
(0.02)
Log Likelihood -15.75 -15.91 -14.63 -15.98 -16.56 -15.21 -16.69
AICc 43.11 43.54 43.58 43.67 44.73 44.89 45.00
Delta 0.00 0.42 0.47 0.56 1.62 1.77 1.88
Weight 0.08 0.06 0.06 0.06 0.04 0.03 0.03
Num. obs. 41 41 41 41 41 41 41
p < 0.001, p < 0.01, p < 0.05

Relationships between different species of Bats

Fire bats

## ~/Documents/new_bats/Rnew_bats/Bats_data_products/fire.asc has GDAL driver AAIGrid 
## and has 250 rows and 434 columns
## ~/Documents/new_bats/Rnew_bats/Bats_data_products/not_fire.asc has GDAL driver AAIGrid 
## and has 250 rows and 434 columns

with.fire without.fire with.fire.sd
Yuma.Myotis 0.23 0.18 0.41
Small.Footed.Myotis 0.15 0.09 0.33
Little.Brown.Bat 0.13 0.21 0.26
Western.Red.Bat 0.12 0.53 0.31
Long.eared.Bat 0.22 0.65 0.39
Pallid.Bat 0.09 0.16 0.28
Townsend.s.big.eared.Bat 0.16 0.19 0.35
Big.Brow.Bat 0.21 0.35 0.39
Silver.Haired.Bat 0.18 0.49 0.36
Brazilian.free.tailed.bat 0.19 0.74 0.37
western.mastiff.bat 0.01 0.15 0.07
## 
##  One Sample t-test
## 
## data:  myyu
## t = 23.888, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.1818892
## 95 percent confidence interval:
##  0.2256631 0.2334887
## sample estimates:
## mean of x 
## 0.2295759
## 
##  One Sample t-test
## 
## data:  myci
## t = 36.501, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.09200702
## 95 percent confidence interval:
##  0.1482991 0.1546876
## sample estimates:
## mean of x 
## 0.1514933
## 
##  One Sample t-test
## 
## data:  mylu
## t = -57.863, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.2051132
## 95 percent confidence interval:
##  0.1304989 0.1353882
## sample estimates:
## mean of x 
## 0.1329435
## 
##  One Sample t-test
## 
## data:  labl
## t = -264.62, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.5267009
## 95 percent confidence interval:
##  0.1164016 0.1224351
## sample estimates:
## mean of x 
## 0.1194183
## 
##  One Sample t-test
## 
## data:  myev
## t = -229.73, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.6525984
## 95 percent confidence interval:
##  0.2128346 0.2202750
## sample estimates:
## mean of x 
## 0.2165548
## 
##  One Sample t-test
## 
## data:  anpa
## t = -52.936, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.1632505
## 95 percent confidence interval:
##  0.08879395 0.09411075
## sample estimates:
##  mean of x 
## 0.09145235
## 
##  One Sample t-test
## 
## data:  coto
## t = -21.478, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.1948568
## 95 percent confidence interval:
##  0.1550577 0.1617141
## sample estimates:
## mean of x 
## 0.1583859
## 
##  One Sample t-test
## 
## data:  epfu
## t = -75.202, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.3493452
## 95 percent confidence interval:
##  0.2018752 0.2093671
## sample estimates:
## mean of x 
## 0.2056211
## 
##  One Sample t-test
## 
## data:  lano
## t = -173.63, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.4905386
## 95 percent confidence interval:
##  0.1775398 0.1845274
## sample estimates:
## mean of x 
## 0.1810336
## 
##  One Sample t-test
## 
## data:  tabr
## t = -301.07, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.7393856
## 95 percent confidence interval:
##  0.1895036 0.1966171
## sample estimates:
## mean of x 
## 0.1930604
## 
##  One Sample t-test
## 
## data:  eupe
## t = -413.34, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.1519613
## 95 percent confidence interval:
##  0.008627646 0.009980571
## sample estimates:
##   mean of x 
## 0.009304108

library(vioplot)
## Loading required package: sm
## Package 'sm', version 2.2-5.4: type help(sm) for summary information
vioplot(myyu,myci, mylu, labl, myev, anpa, coto, epfu, lano, tabr, eupe, col="grey")

The End